Anomaly Detection
Anomaly detection focuses on identifying unusual patterns or deviations from expected behavior within data, aiming to improve system reliability and safety across diverse applications. Current research emphasizes unsupervised and self-supervised learning approaches, employing architectures like autoencoders, transformers, and graph neural networks, often incorporating techniques such as Bayesian inference and metric learning to enhance robustness and interpretability. The field's significance stems from its broad applicability, ranging from fraud detection and medical diagnosis to industrial process monitoring and network security, with ongoing efforts to develop more efficient, accurate, and explainable methods.
Papers
Anatomy-aware Self-supervised Learning for Anomaly Detection in Chest Radiographs
Junya Sato, Yuki Suzuki, Tomohiro Wataya, Daiki Nishigaki, Kosuke Kita, Kazuki Yamagata, Noriyuki Tomiyama, Shoji Kido
Deep Federated Anomaly Detection for Multivariate Time Series Data
Wei Zhu, Dongjin Song, Yuncong Chen, Wei Cheng, Bo Zong, Takehiko Mizoguchi, Cristian Lumezanu, Haifeng Chen, Jiebo Luo
Object Class Aware Video Anomaly Detection through Image Translation
Mohammad Baradaran, Robert Bergevin
A Contrario multi-scale anomaly detection method for industrial quality inspection
Matías Tailanian, Pablo Musé, Álvaro Pardo
Explainable multi-class anomaly detection on functional data
Mathieu Cura, Katarina Firdova, Céline Labart, Arthur Martel
ARCADE: Adversarially Regularized Convolutional Autoencoder for Network Anomaly Detection
Willian T. Lunardi, Martin Andreoni Lopez, Jean-Pierre Giacalone
TracInAD: Measuring Influence for Anomaly Detection
Hugo Thimonier, Fabrice Popineau, Arpad Rimmel, Bich-Liên Doan, Fabrice Daniel